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Host-cell cysteine proteases play an essential role in the processing of the viral spike protein of SARS coronaviruses. K777, an irreversible, covalent inactivator of cysteine proteases that has recently completed phase 1 clinical trials, reduced SARS-CoV-2 viral infectivity in several host cells: Vero E6 (EC50 10 μM. There was no toxicity to any of the host cell lines at 10-100 μM K777 concentration. Kinetic analysis confirmed that K777 was a potent inhibitor of human cathepsin L, whereas no inhibition of the SARS-CoV-2 cysteine proteases (papain-like and 3CL-like protease) was observed. Treatment of Vero E6 cells with a propargyl derivative of K777 as an activity-based probe identified human cathepsin B and cathepsin L as the intracellular targets of this molecule in both infected and uninfected Vero E6 cells. However, cleavage of the SARS-CoV-2 spike protein was only carried out by cathepsin L. This cleavage was blocked by K777 and occurred in the S1 domain of the SARS-CoV-2 spike protein, a different site from that previously observed for the SARS-CoV-1 spike protein. These data support the hypothesis that the antiviral activity of K777 is mediated through inhibition of the activity of host cathepsin L and subsequent loss of cathepsin L-mediated viral spike protein processing.

The COVID-19 pandemic resulted from global infection by the SARS-CoV-2 coronavirus and rapidly emerged as an urgent health issue requiring effective treatments. To initiate infection, the Spike protein of SARS-CoV-2 requires proteolytic processing mediated by host proteases. Among the host proteases proposed to carry out this activation is the cysteine protease cathepsin L. Inhibiting cathepsin L has been proposed as a therapeutic strategy for treating COVID-19. SLV213 (K777) is an orally administered small molecule protease inhibitor that exhibits in vitro activity against a range of viruses, including SARS-CoV-2. To confirm efficacy in vivo, K777 was evaluated in an African green monkey (AGM) model of COVID-19. A pilot experiment was designed to test K777 in a prophylactic setting, animals were pre-treated with 100mg/kg K777 (N=4) or vehicle (N=2) before inoculation with SARS-CoV-2. Initial data demonstrated that K777 treatment reduced pulmonary pathology compared to vehicle-treated animals. A second study was designed to test activity in a therapeutic setting, with K777 treatment (33 mg/kg or 100 mg/kg) initiated 8 hours after exposure to the virus. In both experiments, animals received K777 daily via oral gavage for 7 days. Vehicle-treated animals exhibited higher lung weights, pleuritis, and diffuse alveolar damage. In contrast, lung pathology was reduced in K777-treated monkeys, and histopathological analyses confirmed the lack of diffuse alveolar damage. Antiviral effects were further demonstrated by quantitative reductions in viral load of samples collected from upper and lower airways. These preclinical data support the potential for early SLV213 treatment in COVID-19 patients to prevent severe lung pathology and disease progression.

The application of translational medicine principles coincided with the advent of large-scale molecular profiling approaches (collectively referred to as “OMIC” approaches) which enabled scientists to evaluate biology on previously unprecedented scales and scope. OMIC approaches enable translational understanding in many ways, but one of the primary values of applying OMIC approaches to translational medicine lies in their ability to enable unbiased, empirical biomarker identification for various scientific questions of translational interest. The proper application of OMIC approaches in preclinical and clinical settings can provide researchers with a global view of molecular events in both target and nontarget tissues whether prior to disease onset, during the disease process, or after intervention via exposure to one or more therapeutics. This chapter reviews the principles of these approaches and how they can be applied to translational medicine questions of relevance in drug development.

K777 is a di-peptide analog that contains an electrophilic vinyl-sulfone moiety and is a potent, covalent inactivator of cathepsins. Vero E6, HeLa/ACE2, Caco-2, A549/ACE2, and Calu-3, cells were exposed to SARS-CoV-2, and then treated with K777. K777 reduced viral infectivity with EC50 values of inhibition of viral infection of: 74 nM for Vero E6, 50 values in the low micromolar range. No toxicity of K777 was observed for any of the host cells at 10-100 μM inhibitor. K777 did not inhibit activity of the papain-like cysteine protease and 3CL cysteine protease, encoded by SARS-CoV-2 at concentrations of ≤ 100 μM. These results suggested that K777 exerts its potent anti-viral activity by inactivation of mammalian cysteine proteases which are essential to viral infectivity. Using a propargyl derivative of K777 as an activity-based probe, K777 selectively targeted cathepsin B and cathepsin L in Vero E6 cells. However only cathepsin L cleaved the SARS-CoV-2 spike protein and K777 blocked this proteolysis. The site of spike protein cleavage by cathepsin L was in the S1 domain of SARS-CoV-2, differing from the cleavage site observed in the SARS CoV-1 spike protein. These data support the hypothesis that the antiviral activity of K777 is mediated through inhibition of the activity of host cathepsin L and subsequent loss of viral spike protein processing.SIGNIFICANCEThe virus causing COVID-19 is highly infectious and has resulted in a global pandemic. We confirm that a cysteine protease inhibitor, approved by the FDA as a clinical-stage compound, inhibits SARS-CoV-2 infection of several human and monkey cell lines with notable(nanomolar) efficacy. The mechanism of action of this inhibitor is identified as a specific inhibition of host cell cathepsin L. This in turn inhibits host cell processing of the coronaviral spike protein, a step required for cell entry. Neither of the coronaviral proteases are inhibited, and the cleavage site of spike protein processing is different from that reported in other coronaviruses. Hypotheses to explain the differential activity of the inhibitor with different cell types are discussed.

It is 10 years since the introduction of trastuzumab into the US market, and we are still waiting for a validation of the business case for biomarker-driven drug development. While many reasons for the lack of duplication of this model may exist, the need for accelerated innovation in drug development paired with the opportunity of integrating biomarker-driven research into drug development programs may lead to new and creative ways of fostering the cooperation between drug developers and test manufacturers. The rapid increase in knowledge about biomarkers and our understanding of disease and disease mechanisms open unprecedented prospects to make not only better, more informed decisions regarding patient care, but also strategic decisions during drug development. This requires that a biomarker strategy becomes an integral part of (early) drug development and that new, innovative paths are tried towards a model that combines the scientific approach with an economically feasible implementation strategy. Collaborative research, the use of new communication tools, the exploration of alternative ways to position a product in the market, and other considerations are part of such a strategy. This perspective article illustrates the current landscape and takes a look at some of these new ways for more effectively integrating biomarkers into drug development.

Felix W Frueh, PhD, is a Vice President of Medco Health Solutions (NJ, USA) where he leads Medco’s personalized medicine research and development organization. In this function, Dr Frueh manages Medco’s expanding research efforts in personalized medicine, leveraging the newest developments in science to improve the safety and effectiveness of prescription drug care. He is also responsible for the development of Medco’s Personalized Medicine Research Center which is under construction in Whitestown (IN, USA). Prior to joining Medco, Dr Frueh was an Associate Director for Genomics at the US FDA, where he built up and led the core genomics review team in the Center for Drug Evaluation and Research, and chaired the first FDA-wide, interdisciplinary pharmacogenomics review group. Prior to the FDA, he was a managing partner at Stepoutside Consulting, LLC (MD, USA) and held senior positions at Transgenomic (NE, USA) and Protogene Laboratories (CA, USA). Dr Frueh’s academic career includes a faculty appointment at the Departments of Pharmacology and Medicine at Georgetown University in Washington DC, USA, and postdoctoral fellowships at Stanford University (CA, USA) as well as the University of Basel, Switzerland, where he also received his PhD in biochemistry. Dr Frueh lives in Maryland, USA, with his wife and two sons.

The clinical utility of a molecular test rises proportional to a favorable regulatory risk/benefit assessment, and clinical utility is the driver of payer coverage decisions. Although a great deal has been written about clinical utility, debates still center on its 'definition.' We argue that the definition (an impact on clinical outcomes) is self-evident, and improved communications should focus on sequential steps in building and proving an adequate level of confidence for the diagnostic test's clinical value proposition. We propose a six-part framework to facilitate communications between test developers and health technology evaluators, relevant to both regulatory and payer decisions.

An ever-expanding understanding of the molecular basis of the more than 200 unique diseases collectively called cancer, combined with efforts to apply these insights to clinical care, is forming the foundation of an era of personalized medicine that promises to improve cancer treatment. At the same time, these extraordinary opportunities are occurring in an environment of intense pressure to contain rising healthcare costs. This environment presents a challenge to oncology research and clinical care, because both are becoming progressively more complex and expensive, and because the current tools to measure the cost and value of advances in care (e.g., comparative effectiveness research, cost-effectiveness analysis, and health technology assessments) are not optimized for an ecosystem moving toward personalized, patient-centered care. Reconciling this tension will be essential to maintaining progress in a cost-constrained environment, especially because emerging innovations in science (e.g., increasing identification of molecular biomarkers) and in clinical process (implementation of a learning healthcare system) hold potential to dramatically improve patient care, and may ultimately help address the burden of rising costs. For example, the rapid pace of innovation taking place within oncology calls for increased capability to integrate clinical research and care to enable continuous learning, so that lessons learned from each patient treated can inform clinical decision making for the next patient. Recognizing the need to define the policies required for sustained innovation in cancer research and care in an era of cost containment, the stakeholder community must engage in an ongoing dialogue and identify areas for collaboration. This article reflects and seeks to amplify the ongoing robust discussion and diverse perspectives brought to this issue by multiple stakeholders within the cancer community, and to consider how to frame the research and regulatory policies necessary to sustain progress against cancer in an environment of constrained resources. Clin Cancer Res; 20(5); 1081–6. ©2014 AACR.

There is undisputed evidence that personalized medicine, that is, a more precise assessment of which medical intervention might best serve an individual patient on the basis of novel technology, such as molecular profiling, can have a significant impact on clinical outcomes. The field, however, is still new, and the demonstration of improved effectiveness compared with standard of care comes at a cost. How can we be sure that personalized medicine indeed provides a measurable clinical benefit, that we will be able to afford it, and that we can provide adequate access? The risk-benefit evaluation that accompanies each medical decision requires not only good clinical data but also an assessment of cost and infrastructure needed to provide access to technology. Several examples from the last decade illustrate which types of personalized medicines and diagnostic tests are easily being taken up in clinical practice and which types are more difficult to introduce. And as regulators and payers in the United States and elsewhere are taking on personalized medicine, an interesting convergence can be observed: better, more complete information for both approval and coverage decisions could be gained from a coordination of regulatory and reimbursement questions. Health economics and outcomes research (HEOR) emerges as an approach that can satisfy both needs. Although HEOR represents a well-established approach to demonstrate the effectiveness of interventions in many areas of medical practice, few HEOR studies exist in the field of personalized medicine today. It is reasonable to expect that this will change over the next few years.

We surveyed 10,303 United States physicians on where they obtain pharmacogenomic testing information. Thirty-nine percent indicated that they obtained this from drug labeling. Factors positively associated with this response included older age, postgraduate instruction, using other information sources, regulatory approval/ recommendation of testing, reliance on labeling for information, and perception that patients have benefited from testing. Physicians use pharmacogenomic testing information from drug labeling, highlighting the importance of labeling information that is conducive to practice application.

To develop a benchmark measure of US physicians' level of knowledge and extent of use of pharmacogenomic testing, we conducted an anonymous, cross-sectional, fax-based, national survey. Of 397,832 physicians receiving the survey questionnaire, 10,303 (3%) completed and returned it; the respondents were representative of the overall US physician population. The factors associated with the decision to test were evaluated using χ(2) and multivariate logistic regression. Overall, 97.6% of responding physicians agreed that genetic variations may influence drug response, but only 10.3% felt adequately informed about pharmacogenomic testing. Only 12.9% of physicians had ordered a test in the previous 6 months, and 26.4% anticipated ordering a test in the next 6 months. Early and future adopters of testing were more likely to have received training in pharmacogenomics, but only 29.0% of physicians overall had received any education in the field. Our findings highlight the need for more effective physician education on the clinical value, availability, and interpretation of pharmacogenomic tests.

The focus of treating an individual patient is the identification of the individual's specific needs. The measurement of the patient's characteristics, such as blood pressure or body temperature, and also the measurement of biomarkers, such as cholesterol or hemoglobin A1C is part of the patient's health assessment. The deeper the insights into the phenotypic and molecular characteristics of the patient, the better we are positioned to treat a patient. Increasingly, this assessment includes testing for certain pharmacologically relevant genetic variations (pharmacogenetics). Evaluating how the patient's genetic makeup combined with the patient's exposure to environmental influences could impact disease and treatment decisions is becoming the cornerstone of personalized medicine. However, we often use such assessments for finding the most 'effective' treatment, but we might not always be as rigorous in our assessment of potential safety risks. This is particularly apparent when looking at how safety risks are communicated. Often this information is only available as general, population-based statements and a small amount of information is available to evaluate whether or not an individual patient is at risk. Although pharmacogenetic tests that can help to assess whether an individual patient's personal risk exist (safety pharmacogenetics), they are not always performed.

In light of the meeting of the US Food and Drug Administration (FDA) in March 2011 to discuss the regulation of clinical direct-to-consumer (DTC) genetic tests, we have invited five experts to consider the best means of overseeing the ordering and interpretation of these tests. Should these tests be regulated? If so, who, if anyone, should communicate results to consumers?

Study Objective. To investigate the potential impact of proton pump inhibitors (PPIs) on the effectiveness of clopidogrel in preventing recurrent ischemic events after percutaneous coronary intervention (PCI) with stent placement. Design. Population-based, retrospective cohort study. Data Source. National medical and pharmacy benefit claims database comprising approximately 19 million members. Patients. A total of 16,690 patients who had undergone PCI with stent placement and who were highly adherent to clopidogrel therapy alone (9862 patients) or to clopidogrel with a PPI (6828 patients) between October 1, 2005, and September 30, 2006. Measurements and Main Results. The primary end point was the occurrence of a major adverse cardiovascular event during the 12 months after stent placement. These events were defined as hospitalization for a cerebrovascular event (stroke or transient ischemic attack), an acute coronary syndrome (myocardial infarction or unstable angina), coronary revascularization (PCI or coronary artery bypass graft), or cardiovascular death. A composite event rate was compared between patients who received clopidogrel alone and those who received concomitant clopidogrel-PPI therapy. Baseline differences in covariates were adjusted by using Cox proportional hazards models. In the 9862 patients receiving clopidogrel alone, 1766 (17.9%) experienced a major adverse cardiovascular event compared with 1710 patients (25.0%) who received concomitant clopidogrel-PPI therapy (adjusted hazard ratio 1.51, 95% confidence interval 1.39–1.64, p

The 4th US FDA/Industry workshop, in a series on Pharmacogenomics, was on ‘Biomarkers and Pharmacogenomics in Drug Development and Regulatory Decision Making’ and was held on December 10–12, 2007 in Bethesda, MD, USA, with clear objectives to continue the dialogue that began in 2002 for enabling the use of biomarkers and pharmacogenomics in drug development and regulatory decision-making. This brief commentary will highlight the major topics and outcomes discussed at this meeting that was jointly sponsored by the FDA, The Pharmacogenomics Working Group (PWG), The Pharmaceutical Research and Manufacturers of America (PhRMA), The Biotechnology Industry Organization (BIO) and The Drug Information Association (DIA).

Study Objectives. To review the labels of United States Food and Drug Administration (FDA)-approved drugs to identify those that contain pharmacogenomic biomarker information, and to collect prevalence information on the use of those drugs for which pharmacogenomic information is included in the drug labeling. Design. Retrospective analysis. Data Sources. The Physicians' Desk Reference Web site, Drugs@FDA Web site, and manufacturers' Web sites were used to identify drug labels containing pharmacogenomic information, and the prescription claims database of a large pharmacy benefits manager (insuring > 55 million individuals in the United States) was used to obtain drug utilization data. Measurements and Main Results. Pharmacogenomic biomarkers were defined, FDA-approved drug labels containing this information were identified, and utilization of these drugs was determined. Of 1200 drug labels reviewed for the years 1945–2005, 121 drug labels contained pharmacogenomic information based on a key word search and follow-up screening. Of those, 69 labels referred to human genomic biomarkers, and 52 referred to microbial genomic biomarkers. Of the labels referring to human biomarkers, 43 (62%) pertained to polymorphisms in cytochrome P450 (CYP) enzyme metabolism, with CYP2D6 being most common. Of 36.1 million patients whose prescriptions were processed by a large pharmacy benefits manager in 2006, about 8.8 million (24.3%) received one or more drugs with human genomic biomarker information in the drug label. Conclusion. Nearly one fourth of all outpatients received one or more drugs that have pharmacogenomic information in the label for that drug. The incorporation and appropriate use of pharmacogenomic information in drug labels should be tested for its ability to improve drug use and safety in the United States.

Biologics can be seen as "designer" drugs whose mode of action in a specific disease mechanism is frequently well understood, making it often possible to predict better efficacy and safety profiles for biologics when compared with small molecule drugs. Biologics have been approved for the treatment of major disease classes, such as inflammatory disease, cardiovascular disease, and cancer. However, as it is true for small molecule drugs, often only a fraction of the treated population responds to biologics, and clinical markers for prediction of efficacy are seldom available. It is reasonable to expect that the use of genetic or genomic markers will contribute to improving the prediction of safety and efficacy of both biologics and small molecule drugs. In this paper, we will review the differences between biologics and small molecule drugs, focusing on studies highlighting the relevance of genetic and genomic information on safety and efficacy issues in therapies with biologics. The potential impact of these studies on the promotion of personalized medicine and on regulatory decisions will also be discussed.

The application of the knowledge from the Human Genome Project to clinical medicine will be through both industrial drug development and the application of pharmacogenomics (PG) to patient care. The slow uptake of clinical innovations into clinical practice can be frustrating, but understanding the history of acceptance and sustaining medical innovation is critically important to position PG to succeed. This primarily means that PG tests must have legitimacy; they must be thoroughly validated, must be cost-effective, must be widely accepted by medical practitioners, must be supported by public policy, and must have a way of being easily incorporated into current medical practice. They must also lead to actionalble decisions by health care providers for their patients. Innovative PG assays should be tested in the best US laboratories, and reimbursement for testing must be accepted at the federal and state level. The companies providing these PG tests should be capable of supporting the interpretation and use of the test throughout medical practice. Advances such as the addition of PG information to drug labeling and the routine use of validated biomarkers to determine choice of cancer chemotherapy have been made. The PG research community must pay attention to the principles that have been previously described for acceptance and sustaining medical innovations in order for PG to be widely accepted in clinical medical practice.

The FDA Guidance for Industry: Pharmacogenomics Data Submissions was issued in 2005. This guidance document covers a broad area associated with how and when to submit genomic data to the FDA. Additional tasks associated with genomic data submissions include the implementation of genomic data submissions; the process for qualification of exploratory biomarkers into valid biomarkers; and technical recommendations for the generation and submission of genomic data to the FDA. These tasks have been addressed throughout the past 2 years by a number of initiatives. These initiatives have included the development of the Interdisciplinary Pharmacogenomics Review Group for review of pharmacogenomic data submissions, the pilot process for qualification of biomarkers, and the concept paper on recommendations for the generation and submission of genomic data. These initiatives have contributed to the effective implementation of the Pharmacogenomics Guidance at the FDA.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Current advances in genomics, proteomics, and metabonomics would result in a constellation of benefits in human health. Classification applying supervised learning methods to omics data as one of the molecular classification approaches has enjoyed its growing role in clinical application. However, the utility of a molecular classifier will not be fully appreciated unless its quality is carefully validated. A clinical omics data is usually noisy with the number of independent variables far more than the number of subjects and, possibly, with a skewed subject distribution. Given that, the consensus approach holds an advantage over a single classifier. Thus, the focus of this review is mainly placed on how validating a molecular classifier using Decision Forest (DF), a robust consensus approach. We recommended that a molecular classifier has to be assessed with respect to overall prediction accuracy, prediction confidence and chance correlation, which can be readily achieved in DF. The commonalities and differences between external validation and cross-validation are also discussed for perspective use of these methods to validate a DF classifier. In addition, the advantages of using consensus approaches for identification of potential biomarkers are also rationalized. Although specific DF examples are used in this review, the provided rationales and recommendations should be equally applicable to other consensus methods.

The goal of personalized medicine is to maximize the likelihood of therapeutic efficacy and to minimize the risk of drug toxicity for an individual patient. One of the major contributors to this concept is pharmacogenomics. Marked interindividual genetic variation contributes significantly to both susceptibility to diseases, and response to drugs. Even though pharmacogenomics is not a new science, the translation of pharmacogenomics into clinical practice (i.e., personalized medicine) has not taken place at the same pace as science is delivering new results. It is felt that a large number of recent pharmacogenomic findings allow bold steps to be taken toward personalized medicine. This review collates a variety of examples that have great potential for immediate and effective introduction into clinical practice. In addition, other exploratory examples with a particular focus on drug safety and targeted cancer therapy are summarized.

The scientific community has been enthusiastic about DNA microarray technology for pharmacogenomic and toxicogenomic studies in the hope of advancing personalized medicine and drug development. The US Food and Drug Administration has been proactive in promoting the use of pharmacogenomic data in drug development and has issued a draft guidance for the pharmaceutical industry on data submissions. However, many challenges and pitfalls are facing the microarray community and regulatory agencies before microarray data can be reliably applied to support regulatory decision making. Four types of factors (i.e., technical, instrumental, computational and interpretative) affect the outcome of a microarray study, and a major concern about microarray studies has been the lack of reproducibility and accuracy. Intralaboratory data consistency is the foundation of reliable knowledge extraction and meaningful crosslaboratory or crossplatform comparisons; unfortunately, it has not been seriously evaluated and demonstrated in every study. Profound problems in data quality have been observed from analyzing published data sets, and many laboratories have been struggling with technical troubleshooting rather than generating reliable data of scientific significance. The microarray community and regulatory agencies must work together to establish a set of consensus quality assurance and quality control criteria for assessing and ensuring data quality, to identify critical factors affecting data quality, and to optimize and standardize microarray procedures so that biologic interpretation and decision-making are not based on unreliable data. These fundamental issues must be adequately addressed before microarray technology can be transformed from a research tool to clinical practices.

The field of pharmacogenetics will soon celebrate its 50th anniversary. Although science has delivered an impressive amount of information in these 50 years, pharmacogenetics has suffered from lack of integration into clinical practice. There are several reasons for this, including the unmet need for education at medical schools and the lack of awareness about the impact of genetic medicine on healthcare in the community. Recently, the FDA announced that it considers pharmacogenomics one of three major opportunities on the critical path to new medical products. This notion by the FDA is filling the regulatory void that existed between drug developers and drug users. However, in order to bring pharmacogenetic testing to the prescription pad successfully, healthcare professionals and policy makers, as well as patients, need to have the necessary background knowledge for making educated treatment decisions. To effectively move pharmacogenetics into everyday medicine, it is therefore imperative for scientists and teachers in the field to take on the challenge of disseminating pharmacogenetic insights to a broader audience.

The qualification of biomarkers of drug safety requires data on many compounds and nonclinical and clinical studies. The cost and effort associated with these qualifications cannot be easily covered by a single pharmaceutical company. Intellectual property associated with safety biomarkers is also held by many different companies. Consortia between different pharmaceutical companies can overcome cost and intellectual property hurdles to biomarker qualification. The Predictive Safety Testing Consortium (PSTC) is a collaborative effort between 16 different pharmaceutical companies to generate data supporting biomarker qualification. This Consortium is coordinated through the C-Path Institute, and currently has five biomarker qualification working groups engaged in this collaboration: nephrotoxicity, hepatotoxicity, vascular injury, myopathy, and non-genotoxic carcinogenicity. These working groups are aided by a data management team and a translational strategy team. Qualification studies of promising biomarkers are already progressing in several of the working groups, and results in the nephrotoxicity working group warranted a data submission to the FDA and EMEA for regulatory qualification of new nephrotoxicity biomarkers.

Pharmacogenomics (PGx) is identified in the FDA Critical Path document as a major opportunity for advancing medical product development and personalized medicine. An integrated bioinformatics infrastructure for use in FDA data review is crucial to realize the benefits of PGx for public health. We have developed an integrated bioinformatics tool, called ArrayTrack, for managing, analyzing and interpreting genomic and other biomarker data (e.g. proteomic and metabolomic data). ArrayTrack is a highly flexible and robust software platform, which allows evolving with technological advances and changing user needs. ArrayTrack is used in the routine review of genomic data submitted to the FDA; here, three hypothetical examples of its use in the Voluntary eXploratory Data Submission (VXDS) program are illustrated.

This article defines and describes best practices for the academic and business community to generate evidence of clinical utility for cancer molecular diagnostic assays. Beyond analytical and clinical validation, successful demonstration of clinical utility involves developing sufficient evidence to demonstrate that a diagnostic test results in an improvement in patient outcomes. This discussion is complementary to theoretical frameworks described in previously published guidance and literature reports by the U.S. Food and Drug Administration, Centers for Disease Control and Prevention, Institute of Medicine, and Center for Medical Technology Policy, among others. These reports are comprehensive and specifically clarify appropriate clinical use, adoption, and payer reimbursement for assay manufacturers, as well as Clinical Laboratory Improvement Amendments–certified laboratories, including those that develop assays (laboratory developed tests). Practical criteria and steps for establishing clinical utility are crucial to subsequent decisions for reimbursement without which high-performing molecular diagnostics will have limited availability to patients with cancer and fail to translate scientific advances into high-quality and cost-effective cancer care. See all articles in this CCR Focus section, “The Precision Medicine Conundrum: Approaches to Companion Diagnostic Co-development.” Clin Cancer Res; 20(6); 1428–44. ©2014 AACR.

This work describes the in situ synthesis of oligonucleotide arrays on glass surfaces. These arrays are composed of features defined and separated by differential surface tension (surface tension arrays). Specifically, photolithographic methods were used to create a series of spatially addressable, circular features containing an amino-terminated organosilane coupled to the glass through a siloxane linkage. Each feature is bounded by a perfluorosilanated surface. The differences in surface energies between the features and surrounding zones allow for chemical reactions to be readily localized within a defined site. The aminosilanation process was analyzed using contact angle, X-ray photoelectron spectroscopy (XPS), and time-of-flight/secondary ion mass spectroscopy (TOF-SIMS). The efficiency of phosphoramidite-based oligonucleotide synthesis on these surface tension arrays was measured by two methods. One method, termed step-yields-by-hybridization, indicates an average synthesis efficiency for all four (A,G,C,T) bases of 99.9 +/- 1.1%. Step yields measured for the individual amidite bases showed efficiencies of 98.8% (dT), 98.0% (dA), 97.0% (dC), and 97.6% (dG). The second method for determining the amidite coupling efficiencies was by capillary electrophoresis (CE) analysis. Homopolymers of dT (40- and 60mer), dA (40mer), and dC (40mer) were synthesized on an NH(4)OH labile linkage. After cleavage, the products were analyzed by CE. Synthesis efficiencies were calculated by comparison of the full-length product peak with the failure peaks. The calculated coupling efficiencies were 98.8% (dT), 96.8% (dA), and 96.7% (dC).

The FDA has developed a Pilot Process for Biomarker Qualification. Initial experience with this process has underscored the care that a long-term approach to biomarker qualification independently of development for specific drugs should have in the assembly of external industry consortia as well as the internal regulatory organization for these qualification efforts. There are complex scientific and clinical issues associated with these qualifications, and it is paramount that the expertise needed for their review be identified so that a comprehensive consensus may be reached at the end of this process. Several frequently asked questions associated with this process are presented in this paper, as well as answers reflecting the Agency's current thinking about this process.

Pharmacogenomics is changing the way drugs are being developed, approved and used. The Gurwitz et al article found in the previous issue is a timely 'call to arms' to ensure that this important field does not remain the wisdom of few, but becomes widely accepted and used. We are at a critical stage in pharmacogenomics: the science is not new, but has experienced a significant boost since the human genome project has been completed. Now is the time to capitalize on what basic science has provided and translate it into clinical practice. However, this can only happen if physicians and other health-care professionals, as well as patients, are being educated and become knowledgeable about pharmacogenomics.

Despite the clinical benefit of statin therapy and the numerous strategies used to improve adherence, no strategy has used direct communication of genetic test results to the patient as an adherence and persistence motivator. We investigated in a real-world setting the effect of a process of providing KIF6 test results and risk information directly to 647 tested patients on 6-month statin adherence (proportion of days covered (PDC)) and persistence compared with concurrent non-tested matched controls. Adjusted 6-month statin PDC was significantly greater in tested patients: 0.77 (95% confidence interval (CI) 0.72-0.82) vs controls 0.68 (95% CI 0.63-0.73), P

Background A recent meta-analysis demonstrated that cardiovascular (CV) events and all-cause mortality are reduced in both sexes with statin therapy. Additionally, the Institute of Medicine and the American College of Cardiology advocate reporting of sex-specific CV treatment data. However, females are underrepresented in most CV clinical trials. We investigated the relationship between sex and other patient characteristics with regard to statin therapy adherence and persistence. Methods AKROBATS ( NCT01068834 ) was a prospective, nonrandomized intervention trial of the effect on statin adherence and persistence of providing patients with information about KIF6 carrier status and their KIF6 test results. Eligible patients were ≥18 years old, new to statin therapy (none 6 months prior), and members of the same benefit plans. Controls and tested patients were matched for age, sex, prescription distribution channel, and number of chronic medications. The primary endpoint was statin adherence (proportion of days covered; PDC) at 6 months. Results The study included 1294 patients (582 males & 712 females) (Figure). There was no significant difference in beta blocker (22.3% vs 18.7%), calcium channel blocker (13.7% vs 16.0%), angiotensin-converting enzyme inhibitor, (22.9% vs 19.4%), or angiotensin receptor blocker (13.7% vs 14.2%) use in males compared with females. More females received diuretics (30.6% vs 19.6%) and aspirin/non-steroidal anti-inflammatory agents (16.3% vs 13.2%) than males, p KIF6 testing, distribution channel, number of chronic medications, statin out-of-pocket spend, and income. Independent of sex, more KIF6 -tested patients were adherent (PDC >0.80; OR 1.95, 95% CI 1.54 - 2.47, p Conclusions In this trial with a majority of females (55%), 6-month adherence and persistence to statin therapy did not vary by sex, and was significantly increased by individual knowledge of KIF6 test results in both males and females.

Numerous strategies to improve statin adherence have been evaluated; however, none have utilized direct patient communication of genetic information as a motivator. This trial is the first to evaluate this novel technique. AKROBATS ([NCT01068834][1]) was a prospective, nonrandomized intervention

Teaching old dogs new tricks is difficult, but lessons learned from such efforts can be invaluable. Warfarin is an old drug, difficult to administer and a leading cause of drug-related mortality and hospitalizations. New genetic tests for optimizing warfarin therapy have not been adopted. The debate over precise clinical utility and cost-effectiveness of these tests misses more important points of building a better, cheaper, and more efficient infrastructure to measure the true real-world impact of personalized medicine. However, this same debate about how, when, and where such testing is appropriate has been invaluable to the field of personalized medicine: progress beyond science, in policy, regulations, and logistics can be highlighted along the path to safer and more efficacious warfarin therapy.

The US FDA encourages the integration of biomarkers in drug development and their appropriate use in clinical practice. It is believed that this approach will help alleviate stagnation and foster innovation in the development of new medical products, and, ultimately, lead to more personalized medicine. To facilitate the use of biomarkers in drug development and clinical practice, the FDA organized workshops, issued guidances, established a voluntary submission process, developed online educational tools and, most importantly, strives to ensure the integration of this information into drug labels, for example, via the update of existing labels, or the inclusion of appropriate language in new drug labels. A pilot process has been set up to qualify novel biomarkers that are not associated with specific drug products, but are of more common use (e.g., biomarkers for drug safety). In addition, the FDA has initiated the creation of various consortia that are working towards the identification and characterization of exploratory biomarkers in order to qualify them for a specific use.

Many factors can affect a patient's response to a drug. These include intrinsic factors such as age, gender, race/ethnicity, genetics, disease states, organ dysfunctions, and other physiological changes, including pregnancy, lactation, and extrinsic factors such as smoking, diet (food, juice, dietary supplements), and concomitant medications (ICH E5, 1998 and 2004). The interplay of genotypes of the enzymes, transporters and receptors, among other factors (such as concomitant medications and disease states), can affect the risk/benefit ratio for individual patients. This commentary discusses when the genomic information should be obtained during drug development and when it is to be assimilated into labeling and standards of care that can be used to "individualize" drug therapy and become one of the pillars of "personalized medicine."

Randomized controlled trials are the gold standard for determining the efficacy of therapeutic interventions. However, medical practice has not evolved around the concept of randomized trials, but around the idea of careful observations, (anecdotal) case studies and the evaluation of retrospective data. Interventions discovered by these means and taken forward into clinical practice became standard practice as they continued to be superior when compared with prior or alternative types of treatment. Personalized medicine refers to an approach of clinical practice where a particular treatment is not chosen based on the ‘average patient’, but on characteristics of an individual patient, for example, a genetic profile that may vary from one patient to another, and therefore, allows to ‘personalize’ the treatment to a patient’s individual needs. While the call for prospective randomized controlled trials to assess the effective use of such measurement may make sense in some cases, it is, when applied without distinction, hindering the implementation of personalized medicine. Important evidence for the validity and clinical effectiveness of using biomarkers, for example, a patient’s genetic profile, can be gained from alternative approaches, including case–control and cohort studies, and retrospective analyses of data. Hence, we need to re-focus on approaches that are neither new nor unproven, but have been ignored over the last few decades.

A series of questions about hypothetical drugs and pharmacogenomic tests was posed to a panel of representatives from the health plan, government and employer sectors in order to elicit suggestions for input on data or study design considerations important for coverage determination. The panel suggested seven areas for drug developers to strongly consider. These areas were to include comparative information on new tests versus usual care, assess the negative predictive value of new tests, measure and report on cost offsets, balance relative risk improvement with absolute risk, consider the policy implications of the products or tests, report percentage responders in addition to group mean improvements, and to include specific pharmacogenomic information in US FDA approved labels. The panel was generally enthusiastic about the promise of the field to improve drug selection or dosing.

Despite their growing popularity in the research community, DNA microarray-based gene expression assays have not yet been approved or licensed by the U.S. Food and Drug Administration for clinical or regulatory decision-making. While recent publications demonstrated the technical reliability of microarray technology, there are several challenges that still need to be addressed so that microarray-based gene expression data can be accepted for routine use in clinical and regulatory environments. First, appropriate quality-control metrics and thresholds are needed for objectively assessing the quality of microarray data from individual laboratories. Secondly, consensus on the analysis of microarray data is needed. Thirdly, adequate evaluation and validation of microarray results is needed so that non-reproducible chance correlations can be avoided in handling high-dimensional microarray data.

The drug development process is dependent upon having established end points for measuring drug efficacy and adverse effects. New drug development in organ transplantation suffers from having end points which are either outdated or which do not serve the purpose of addressing the current critical drug therapy problems. Numerous biomarkers have been examined in organ transplantation, but almost all would be classified as exploratory for drug development purposes. Some of the possible pathways out of this dilemma include investigator- or consortium-initiated research that would qualify the biomarkers as either probable or known valid biomarkers, help in identification of new end points in transplantation and their associated biomarkers, co-development of a new biomarker and drug for transplantation and the use of new clinical trial design methods which facilitate enriched or stratified transplant patient populations. With new biomarkers and new study design methodologies for drug development, improvement in the drug development process for transplantation is a real possibility that the transplant clinical and research community can help to bring about.

Biomarkers may be qualified using different qualification processes. A passive approach for qualification has been to accept the end of discussions in the scientific literature as an indication that a biomarker has been accepted. An active approach to qualification requires development of a comprehensive process by which a consensus may be reached about the qualification of a biomarker. Active strategies for qualification include those associated with context-independent as well as context-dependent qualifications.

Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity.